Tuesday publication update! 📖 In this recent publication, the #FusionAX system was used to develop a deep learning-based analysis that enables automated unit-cell pattern recognition and precise quantification of interlayer stacking displacements in bilayer phosphorene!
The authors were able to achieve:
⭐ 3.3% error level in unit-cell displacement detection.
⚛️ Near atomic-level spatial resolution for interlayer stacking identification.
💻 Automated analysis of large-scale in situ TEM datasets, capturing time-dependent displacement dynamics during edge reconstruction.
🔥 Scalable for studying a wide range of 2D and layered materials.
This study shows new pathways for tuning electronic and mechanical properties in 2D materials and therefore expands the potential of in situ TEM for real-time materials engineering.
Want to read the entire work? Find it here!
https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202416480
